Autonomous driving, augmented reality and navigation applications often rely on three-dimensional (3D) object detection based on a 3D map or model of a local scene. For example, to take advantage of high-definition 3D maps, autonomous vehicles sense the environment surrounding the vehicle and match the environment to the 3D map using a process called localization. The localization process relies on pertinent objects, structures and other localization objects in the vehicle environment to be present in the 3D map.
Gaps in a 3D map, such as missing objects, structures and/or other localization objects, may be detrimental to the performance of localization algorithms. Gaps in the 3D map often exist due to “false negatives” generated by object detection algorithms, frequently due to missing input data. For example, one reason for missing input data is the presence of a light detection and ranging (LIDAR) occlusion (aka as LIDAR “shadows”), such as when a temporary object (e.g., a semi-truck) is positioned between the LIDAR scanner of a data collection vehicle and pertinent objects on the side of the road intended to be captured. In this example, the LIDAR scanner only captures data of the semi-truck, resulting in a LIDAR occlusion and a false negative for the pertinent roadside objects blocked by the semi-truck.